- Docente: Iliyan Georgiev
- Crediti formativi: 6
- SSD: SECS-P/05
- Lingua di insegnamento: Inglese
- Moduli: Iliyan Georgiev (Modulo 1) Giuseppe Cavaliere (Modulo 2)
- Modalità didattica: Convenzionale - Lezioni in presenza (Modulo 1) Convenzionale - Lezioni in presenza (Modulo 2)
- Campus: Bologna
- Corso: Laurea Magistrale in Economics (cod. 8408)
Conoscenze e abilità da conseguire
At the end of the course the student has acquired an advanced and comprehensive knowledge of the main, up-to-date econometric methods for the analysis of economic and financial time series data. In terms of inference techniques, emphasis is given to up-to-date bootstrap methods. In particular, she/he is able: - to analyze critically the application of advanced econometric models to economic time series data; - to implement and make use of proper (asymptotic and bootstrap) inference methods in dynamic environments.
Contenuti
Part I: Conditional volatility models: estimation, inference and applications
- Univariate GARCH processes: properties, estimation, diagnostics and inference.
- Applications to Value at Risk.
- Multivariate models of conditional variance: estimation, diagnostics and inference.
- Applications to optimal hedging.
Part II: Asymptotic and Bootstrap inference in time series
- Introduction to the bootstrap: iid, wild, fixed regressor, moving block, m out of n, permutation, subsampling
- Bootstrapping stationary time series
- Bootstrap inference in multivariate (VAR) models
- Non-stationary time series: bootstrapping unit root and cointegration tests
- Bootstrapping conditional volatility models and the parameter on the boundary problem
Testi/Bibliografia
Lütkepohl H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Gatarek L., Johansen S. (2015). PDF [https://www.eui.eu/Documents/DepartmentsCentres/Economics/Seminarsevents/Johansen.pdf]
Horowitz J. (2001). The bootstrap. In: Handbook of Econometrics, vol. V.
Lecture notes provided by the instructors
Metodi didattici
Lectures
Modalità di verifica e valutazione dell'apprendimento
Take home exam (possibly followed by an oral discussion, on discretion of the course instructors).
Passing numerical grades are intended to match the following qualitative description:
18-23: sufficient
24-27: good
28-30: very good
30 cum laude: excellent.
Strumenti a supporto della didattica
A dedicated page on virtuale.unibo.it
Orario di ricevimento
Consulta il sito web di Iliyan Georgiev
Consulta il sito web di Giuseppe Cavaliere
SDGs
L'insegnamento contribuisce al perseguimento degli Obiettivi di Sviluppo Sostenibile dell'Agenda 2030 dell'ONU.